关键词: 递归pi-sigma神经网络, 梯度算法, 惩罚项, 收敛性

Abstract: In this paper, a new gradient training algorithm is presented to train the recurrent pi-sigma neural networks, in which a penalty is added to the conventional error function. The algorithm can not only improve the generalization of neural networks, but also avoid the slow convergence caused by the case that the original weights are chosen too small, achieving a better convergence compared to the traditional gradient algorithm without the penalty term. Moreover, the convergence of the algorithm is also studied, and finally the simulated experimental results indicates that the algorithm is efficient.

Key words: recurrent pi-sigma neural networks, gradient algorithm, penalty, convergence